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The Knowledge Graph expands as discipline's conference spreads its wings

ZDNet

Northeastern University professor and best-selling author Albert-László Barabási, who has popularized the graph approach to knowledge in books such as Linked and Bursts, was a recipient Thursday of the Conference's lifetime achievement award. Knowledge graphs are an area of data science concerned with relationships between things. And so it is perhaps no surprise that the relationships between the practitioners of the art keep expanding. The Knowledge Graph Conference, a gathering of graph enthusiasts that began as a couple hundred people in a ballroom at Columbia University in 2019, is now in its third year, with big sponsorship, including Amazon, and a much-extended program. What was a single-track, two-day program has blossomed into a four-day gathering of talks, workshops, tutorials on all manner of topics, with over 1,500 people participating from 50 different countries.


Best Universities for Artificial Intelligence (AI) Programs

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Artificial Intelligence (AI) is one of the most exciting research areas being conducted at the moment, and it has seen much popularity in recent years. Several universities now offer specialized degrees within AI, and some major ones offer a broader focus within Computer Science, Machine Learning, or other quantitative fields. While this list of the best university programs in artificial intelligence is not exhaustive, it provides a critical overview of some universities doing exceptional research in AI. Many of today's biggest tech companies are looking for individuals with a background in artificial intelligence (AI): Facebook, Apple, Google to name a few, focus on this technology. It helps them improve and optimize their core business processes (with these new technologies, Facebook was able to obtain more than 1 billion users registered in a few years).


What a Crossword AI Reveals About Humans' Way With Words

WIRED

At last week's American Crossword Puzzle Tournament, held as a virtual event with more than 1,000 participants, one impressive competitor made news. For the first time, artificial intelligence managed to outscore the human solvers in the race to fill the grids with speed and accuracy. It was a triumph for Dr. Fill, a crossword-solving automaton that has been vying against carbon-based cruciverbalists for nearly a decade. For some observers, this may have seemed like just another area of human endeavor where AI now has the upper hand. Reporting on Dr. Fill's achievement for Slate, Oliver Roeder wrote, "Checkers, backgammon, chess, Go, poker, and other games have witnessed the machines' invasions, falling one by one to dominant AIs. Now crosswords have joined them."


AI in Health Care: Recent Updates

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Andrew Beam, PhD is an assistant professor in the Department of Epidemiology at the Harvard T.H. Chan School of Public Health, with secondary appointments in the Department of Biomedical Informatics at Harvard Medical School and the Department of Newborn Medicine at Brigham and Women's Hospital. His research develops and applies machine-learning methods to extract meaningful insights from clinical and biological datasets, and he is the recipient of a Pioneer Award from the Robert Wood Johnson Foundation for his work on medical artificial intelligence. Previously he was a Senior Fellow at Flagship Pioneering and the founding head of machine learning at Generate Biosciences, Inc., a Flagship-backed venture that seeks to use machine learning to improve our ability to engineer proteins.


Undergraduates explore practical applications of artificial intelligence

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Deep neural networks excel at finding patterns in datasets too vast for the human brain to pick apart. That ability has made deep learning indispensable to just about anyone who deals with data. This year, the MIT Quest for Intelligence and the MIT-IBM Watson AI Lab sponsored 17 undergraduates to work with faculty on yearlong research projects through MIT's Advanced Undergraduate Research Opportunities Program (SuperUROP). Students got to explore AI applications in climate science, finance, cybersecurity, and natural language processing, among other fields. And faculty got to work with students from outside their departments, an experience they describe in glowing terms.


Here's what UC says about the chances of being plucked from massive waitlists

Los Angeles Times

Anika Madan, a senior at Sunny Hills High in Fullerton, had a loaded school resume when she applied to six University of California campuses for admission this fall: a 4.6 GPA, 11 college-level courses, student leadership positions and community service building robotic hands for people with disabilities. She was accepted to UC campuses at Irvine, Riverside and Santa Barbara -- but wait-listed at Berkeley, Davis and San Diego. Once again she is on edge -- along with tens of thousands of others -- as yet another nail-biting phase of a record-breaking UC admission season begins this week. Campuses are diving into their massive waitlists, selecting students to fill the seats of those who turned down UC offers by the May 1 college decision day. For the waitlisted, this next round is sparking more anxiety, frustration and even defiance as they try to decide whether to hold out for an offer from a favored campus or just move on.


Artificial intelligence is infiltrating higher ed, from admissions to grading

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Students newly accepted by colleges and universities this spring are being deluged by emails and texts in the hope that they will put down their deposits and enroll. If they have questions about deadlines, financial aid and even where to eat on campus, they can get instant answers. The messages are friendly and informative. Artificial intelligence, or AI, is being used to shoot off these seemingly personal appeals and deliver pre-written information through chatbots and text personas meant to mimic human banter. It can help a university or college by boosting early deposit rates while cutting down on expensive and time-consuming calls to stretched admissions staffs.


A Beginner's Guide to Regression Analysis in Machine Learning

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In order to understand the motivation behind regression, let's consider the following simple example. The scatter plot below shows the number of college graduates in the US from the year 2001 to 2012. Now based on the available data, what if someone asks you how many college graduates with master's degrees will there be in the year 2018? It can be seen that the number of college graduates with master's degrees increases almost linearly with the year. So by simple visual analysis, we can get a rough estimate of that number to be between 2.0 to 2.1 million. Let's look at the actual numbers.


U.S. Universities Must Rise to Meet the AI Challenge

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Artificial intelligence – the ability of machines to use massive amounts of data and computing power to mimic such human attributes as reasoning – is transforming our world. But is higher education keeping up? The answer will play a major role in determining whether the United States will meet the challenge from China and elsewhere. We are well beyond the days when AI was limited to such science fiction as the famously petulant computer "Hal" in the film "2001: A Space Odyssey." AI now plays a major role in healthcare diagnostics and treatment, transportation, robotics, finance, entertainment, and in higher education itself.


Using Artificial Intelligence Tools to Run Proactive "Health Check" Investigations - insideBIGDATA

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In the legal world, and in particular the world of electronic discovery, artificial intelligence (AI) has been around for more than a decade. It is no longer unusual or controversial for organizations to use AI technologies in litigation, especially where large or complex data sets are involved. Legal teams now routinely turn to AI to defensibly accelerate the process of identifying documents likely to be responsive to requests for evidence. Innovations like technology assisted review (TAR), for example, rely heavily on machine learning and natural language processing to make connections and identify patterns within a body of data in a matter of seconds. This is work that would take even the most qualified human reviewers many, many hours to do manually, and with less accuracy.